{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9c3095f-3301-44f6-a25b-9cd227b138c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting the environment variables\n",
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.insert(0, os.path.abspath('..'))\n",
    "\n",
    "from config import set_environment\n",
    "set_environment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f52cc2d-0d59-471a-bc9f-3c7ccf82c30c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "3072\n"
     ]
    }
   ],
   "source": [
    "from langchain_openai import OpenAIEmbeddings \n",
    "\n",
    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n",
    "text = \"This is a sample query.\" \n",
    "query_result = embeddings.embed_query(text) \n",
    "print(query_result)\n",
    "print(len(query_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2f46109-5967-4c5f-a59d-0ef155251cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(query_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4aec3d4-9c15-4d14-9b53-a7ade48038f3",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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       "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
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       "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
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      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f0b85c36b5d402aaf31f675b93aa260",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef807b17b13b4179817e141a1f5d52aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8c23fe38d914aa1abc97140898c867b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from langchain_community.embeddings import HuggingFaceBgeEmbeddings\n",
    "\n",
    "model_name = \"BAAI/bge-small-en\"\n",
    "model_kwargs = {\"device\": \"cpu\"}\n",
    "encode_kwargs = {\"normalize_embeddings\": True}\n",
    "hf = HuggingFaceBgeEmbeddings(\n",
    "    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa0c1c66-79fd-40b5-a28f-932fe4c31b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = [\"cat\", \"dog\", \"computer\", \"animal\"]\n",
    "doc_vectors = hf.embed_documents(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59cb531b-858e-4c0e-990e-48f4efb6a01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(doc_vectors[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "159f9da5-5a8b-406f-a1fb-4674802f4343",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "X = np.array(doc_vectors)\n",
    "dists = squareform(pdist(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b53b4c3a-6d5d-4775-af77-04da0ed568da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_639b6_row0_col0, #T_639b6_row1_col1, #T_639b6_row2_col2, #T_639b6_row3_col3 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_639b6_row0_col1 {\n",
       "  background-color: #cf453c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_639b6_row0_col2 {\n",
       "  background-color: #ba162b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_639b6_row0_col3 {\n",
       "  background-color: #dc5d4a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_639b6_row1_col0 {\n",
       "  background-color: #ca3b37;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_639b6_row1_col2, #T_639b6_row2_col0, #T_639b6_row2_col1, #T_639b6_row2_col3 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_639b6_row1_col3 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_639b6_row3_col0 {\n",
       "  background-color: #de614d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_639b6_row3_col1 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_639b6_row3_col2 {\n",
       "  background-color: #bb1b2c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_639b6\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_639b6_level0_col0\" class=\"col_heading level0 col0\" >cat</th>\n",
       "      <th id=\"T_639b6_level0_col1\" class=\"col_heading level0 col1\" >dog</th>\n",
       "      <th id=\"T_639b6_level0_col2\" class=\"col_heading level0 col2\" >computer</th>\n",
       "      <th id=\"T_639b6_level0_col3\" class=\"col_heading level0 col3\" >animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_639b6_level0_row0\" class=\"row_heading level0 row0\" >cat</th>\n",
       "      <td id=\"T_639b6_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
       "      <td id=\"T_639b6_row0_col1\" class=\"data row0 col1\" >0.473184</td>\n",
       "      <td id=\"T_639b6_row0_col2\" class=\"data row0 col2\" >0.504708</td>\n",
       "      <td id=\"T_639b6_row0_col3\" class=\"data row0 col3\" >0.440926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_639b6_level0_row1\" class=\"row_heading level0 row1\" >dog</th>\n",
       "      <td id=\"T_639b6_row1_col0\" class=\"data row1 col0\" >0.473184</td>\n",
       "      <td id=\"T_639b6_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
       "      <td id=\"T_639b6_row1_col2\" class=\"data row1 col2\" >0.513160</td>\n",
       "      <td id=\"T_639b6_row1_col3\" class=\"data row1 col3\" >0.260210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_639b6_level0_row2\" class=\"row_heading level0 row2\" >computer</th>\n",
       "      <td id=\"T_639b6_row2_col0\" class=\"data row2 col0\" >0.504708</td>\n",
       "      <td id=\"T_639b6_row2_col1\" class=\"data row2 col1\" >0.513160</td>\n",
       "      <td id=\"T_639b6_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "      <td id=\"T_639b6_row2_col3\" class=\"data row2 col3\" >0.501650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_639b6_level0_row3\" class=\"row_heading level0 row3\" >animal</th>\n",
       "      <td id=\"T_639b6_row3_col0\" class=\"data row3 col0\" >0.440926</td>\n",
       "      <td id=\"T_639b6_row3_col1\" class=\"data row3 col1\" >0.260210</td>\n",
       "      <td id=\"T_639b6_row3_col2\" class=\"data row3 col2\" >0.501650</td>\n",
       "      <td id=\"T_639b6_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1426a7350>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    data=dists,\n",
    "    index=words,\n",
    "    columns=words\n",
    ")\n",
    "df.style.background_gradient(cmap='coolwarm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4157d0d-ec89-4cb8-bebf-0729b4aed368",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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